STAT 454: Capstone 3
Freddy Barragan, Juthi Dewan, Sam Ding, Vichy Meas
18 November, 2021
# Load packages
library(here)
library(dplyr)
library(readr)
library(rstan)
library(bayesrules)
library(tidyverse)
library(bayesplot)
library(rstanarm)
library(janitor)
library(tidybayes)
library(broom.mixed)
library(here)
library(sf)
library(tidycensus)
library(openxlsx)
library(s2)# themes
theme_set(theme_minimal())
vari_names <- read_csv(here("clean_data", "nyc_names.csv"))
nyc_clean <- st_read(here("clean_data", "nyc_data.shp"), crs = 4269) ## Reading layer `nyc_data' from data source
## `/Users/freddy/Documents/GitHub/454/clean_data/nyc_data.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 242 features and 22 fields (with 1 geometry empty)
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25609 ymin: 40.4961 xmax: -73.70002 ymax: 40.91758
## Geodetic CRS: NAD83colnames(nyc_clean) <- colnames(vari_names)subway_stations <- st_read(here("ethnic","Data","stations", "geo_export_85568705-efba-4456-bdc0-3d70ff2cf8e5.shp")) %>%
st_transform(., 4269)## Reading layer `geo_export_85568705-efba-4456-bdc0-3d70ff2cf8e5' from data source
## `/Users/freddy/Documents/GitHub/454/ethnic/Data/stations/geo_export_85568705-efba-4456-bdc0-3d70ff2cf8e5.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 473 features and 5 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -74.03088 ymin: 40.57603 xmax: -73.7554 ymax: 40.90313
## Geodetic CRS: WGS84(DD)bus_stations <- st_read(here("ethnic","Data","bus", "bus_stops_nyc_may2020.shp")) %>%
st_transform(., 4269)## Reading layer `bus_stops_nyc_may2020' from data source
## `/Users/freddy/Documents/GitHub/454/ethnic/Data/bus/bus_stops_nyc_may2020.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 14663 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: 914169.1 ymin: 122626.8 xmax: 1066982 ymax: 271696.8
## Projected CRS: NAD83 / New York Long Island (ftUS)transit_points <- read_csv(here("transit","ridership_points.csv"))%>%
separate(Position, into=c("Point", "longitude", "latitude"), " ") %>%
mutate(latitude = str_remove_all(latitude, "[)]"),
longitude = str_remove_all(longitude, "[()]"),
) %>%
dplyr::select(-c(Point)) %>%
mutate(latitude = as.numeric(latitude),
longitude = as.numeric(longitude)) %>%
st_as_sf(coords = c("longitude", "latitude"), crs = 4269)#plot locations over map
subway_loc <- ggplot() +
geom_sf(data = nyc_clean, fill = "#EBF6FF", color = "#D48DD8", size = 0.15, alpha = .8) +
geom_sf(data = subway_stations, color="#3F123C", size=1) +
coord_sf(datum = st_crs(subway_stations)) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Subway Stop Locations \nin NYC")+
theme(#panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
bus_loc <- ggplot() +
geom_sf(data = nyc_clean, fill = "#EBF6FF", color = "#D48DD8", size = 0.15, alpha = .8) +
geom_sf(data = bus_stations, color="#3F123C", size=.5, alpha=.5) +
coord_sf(datum = st_crs(subway_stations)) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Bus Stop Locations \nin NYC")+
theme(#panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
stops <- nyc_clean %>%
ggplot() +
geom_sf(aes(fill = sub_count), color = "#8f98aa") +
scale_fill_gradient(low= "lavender", high = "maroon",
guide = guide_legend(title = "Number of Subway Stops") ,na.value="#D6D6D6") +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Subway Stop Counts \nin NYC")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
bus_stops <- nyc_clean %>%
ggplot() +
geom_sf(aes(fill = bus_count), color = "#8f98aa") +
scale_fill_gradient(low= "lavender", high = "maroon",
guide = guide_legend(title = "Number of Bus Stops") ,na.value="#D6D6D6") +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Bus Stop Counts \nin NYC")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
ridership <- nyc_clean%>%
ggplot() +
geom_sf(aes(fill = log2(mean_ridership)), color = "#8f98aa") +
scale_fill_gradient(low= "lavender", high = "maroon",
guide = guide_legend(title = "Log2 Mean Ridership") ,na.value="#D6D6D6") +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Mean (Log2) Subway Turnstile \nRidership in 2018 \nfor NYC")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
access <- nyc_clean %>%
ggplot() +
geom_sf(aes(fill = perc_covered_by_transit), color = "#8f98aa") +
scale_fill_gradient(low= "lavender", high = "maroon",
guide = guide_legend(title = "Percent of Neighborhood within \n walking distance of a transit station"), na.value="#D6D6D6") +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Transit Accessibility \nin NYC")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))library(egg)
ggarrange(subway_loc, bus_loc, stops, bus_stops, ridership, access, ncol=2)
`
red <- ggplot(nyc_clean) +
geom_sf(aes(fill = below_poverty_line_count), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#E13728", guide = guide_legend(title = "Number Below \nPoverty Line")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Impoverishement")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
yellow <- ggplot(nyc_clean) +
geom_sf(aes(fill = mean_income), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#F3D24E", guide = guide_legend(title = "Mean Income")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Mean Income")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
teal <- ggplot(nyc_clean) +
geom_sf(aes(fill = mean_rent), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#2DBDC7", guide = guide_legend(title = "Dollars")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Mean Rent")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
purple <- ggplot(nyc_clean) +
geom_sf(aes(fill = eviction_count), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#7826C0", guide = guide_legend(title = "Number of Evictions")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Evictions")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
orange <- ggplot(nyc_clean) +
geom_sf(aes(fill = unemployment_count), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#FC9228", guide = guide_legend(title = "Number on \nUnemployment")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Unemployment")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
green <- ggplot(nyc_clean) +
geom_sf(aes(fill = store_count), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#326902", guide = guide_legend(title = "Number of Stores")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Retail Food Stores")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
blue <- ggplot(nyc_clean) +
geom_sf(aes(fill = school_count), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#5372C4",
guide = guide_legend(title = "Number of Schools")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Number of Schools")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
pink <- ggplot(nyc_clean) +
geom_sf(aes(fill = total_pop), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#F450E1", guide = guide_legend(title = "Number of People")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Population")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
brown <- ggplot(nyc_clean) +
geom_sf(aes(fill = uninsured_count), color = "#8f98aa")+
scale_fill_gradient(low = "#F8E3DD", high = "#6A4D39", guide = guide_legend(title = "Number of People \n without Insurance Coverage")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Insurance Coverage")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))predictors_map <- ggarrange(red, orange, yellow, green, teal, blue, purple, pink, brown, ncol=3)
white <- ggplot(nyc_clean) +
geom_sf(aes(fill = white_count), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#7B435B", guide = guide_legend(title = "Number White")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("White Population")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
black <- ggplot(nyc_clean) +
geom_sf(aes(fill = black_count), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#F25F5C", guide = guide_legend(title = "Number Black")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Black Population")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
asian <- ggplot(nyc_clean) +
geom_sf(aes(fill = asian_count), color = "#8f98aa") +
scale_fill_gradient(low = "#FCF5EE", high = "#717EC3", guide = guide_legend(title = "Number Asian")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Asian Population")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))
latinx <- ggplot(nyc_clean) +
geom_sf(aes(fill = latinx_count), color = "#8f98aa")+
scale_fill_gradient(low = "#FCF5EE", high = "#FC9A38", guide = guide_legend(title = "Number Latinx")) +
theme_minimal() +
theme(panel.grid.major = element_line("transparent"),
axis.text = element_blank()) +
ggtitle("Latinx Population")+
theme(panel.grid.major = element_line("transparent"),
plot.title = element_text(size = 30, face = "bold"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
guides(shape = guide_legend(override.aes = list(size = 8)),
color = guide_legend(override.aes = list(size = 8)))demographicMap <- ggarrange(white, black, latinx, asian, ncol=2)
Data Introduction
All the data used in this project are from two major sources: the Tidycensus package and NYC Open Data.
Tidycensus is an R package interface, developed by Kyle Walker and Matt Herman, that enables easy access to the US Census Bureau’s data APIs and returns Tidyverse-ready data frames from various major US Census Bureau datasets. Our demographic and socioeconomic data are drawn from the American Community Survey results found in Tidycensus package. A summary of our ACS data variables is below:
total_pop: Total Population by Neighborhood
mean_income: Mean Income by Neighborhood
below_poverty_line_count: Number of People Living Below the 100% Poverty Line by Neighborhood
mean_rent: Mean Rent by Neighborhood
unemployment_count: Number of People on Unemployment by Neighborhood
latinx_count: Number of Latinx People by Neighborhood
white_count: Number of White People by Neighborhood
black_count: Number of Black People by Neighborhood
native_count: Number of Native People by Neighborhood
asian_count: Number of Asian People by Neighborhood
naturalized_citizen_count: Number of Naturalized Citizens by Neighborhood
noncitizen_count: Number of Foreign Born People by Neighborhood
uninsured_count: Number of Uninsured Citizens of any Age by NeighborhoodFor remaining predictors, we used NYC Open Data’s portal to identify specific predictors. In particular, we used geotagged locations of Subway Stops, Bus Stops, Grocery Stores, Schools, and Eviction Sites from the Departments of Transportation, Health, Education, and Housing to calculate neighborhood-specific variables described below:
school_count: Number of Public Schools by Neighborhoodeviction_count: Number of Evictions by Neighborhoodstore_count: Number of Grocery Stores and Food Vendors by Neighborhoodsub_count: Number of Subway Stations by Neighborhoodbus_count: Number of Bus Stations by Neighborhoodperc_covered_by_transit: Percent of Neighborhood within walking distance (.25 miles).Lastly, we acquired subway ridership from Metropolitan Transportation Authority’s turnstile data for the week of September 7, 2019. For each station, entry/exit of each turnstile is recorded. Then, we aggregated this information by taking the station-specific average of subway ridership across the 7 days in the week. Finally, we geotagged each listed station, then took the mean of ridership at all subway stations in each neighborhood.
Data Summaries
dim(nyc_clean)
names(nyc_clean)
head(nyc_clean)
summary(nyc_clean)Data Visuals
Model Building
Models 1 & 2: No Grouping
We fit ungrouped poisson and negative binomial models below:
poisson_non_hierarchical <- stan_glm(
white_count ~ total_pop + mean_income + mean_rent +
unemployment_count + sub_count +
perc_covered_by_transit + school_count +
store_count + sub_count + bus_count +
eviction_count + uninsured_count,
data = nyc_clean,
family = poisson,
chains = 4, iter = 500*2, seed = 84735, refresh = 0
)
negbin_non_hierarchical <- stan_glm(
white_count ~ total_pop + mean_income + mean_rent +
unemployment_count + sub_count +
perc_covered_by_transit + school_count +
store_count + sub_count + bus_count +
eviction_count + uninsured_count,
data = nyc_clean,
family = neg_binomial_2,
chains = 4, iter = 500*2, seed = 84735, refresh = 0
)check_1 <- pp_check(poisson_non_hierarchical) +
xlab("White Resident Count") +
labs(title = "Poisson")+
xlim(0,100000)+
theme(plot.title = element_text(face="bold", size=25, hjust=.5))
check_2 <- pp_check(negbin_non_hierarchical) +
xlab("White Resident Count") +
labs(title = "Negative Binomial")+
xlim(0,100000)+
theme(plot.title = element_text(face="bold", size=25, hjust=.5))
ggpubr::ggarrange(check_1, check_2, ncol=2, common.legend=TRUE, legend = "right")
nyc_predict_clean <- nyc_clean %>%
na.omit()
set.seed(84735)
predictions_poisson <- posterior_predict(
poisson_non_hierarchical, newdata = nyc_predict_clean)
predictions_negbin <- posterior_predict(
poisson_non_hierarchical, newdata = nyc_predict_clean)
library(tidybayes)
library(bayesplot)
ppc_intervals(nyc_predict_clean$white_count, yrep = predictions_poisson,
prob_outer = 0.95) +
ggplot2::scale_x_continuous(
labels = nyc_predict_clean$nta_id,
breaks = 1:nrow(nyc_predict_clean)) +
xaxis_text(angle = 90, hjust = 1) +
theme_linedraw()+
theme(panel.grid.major = element_line("transparent"),
axis.title.y = element_blank(),
axis.text.y = element_blank())+
coord_flip()
ppc_intervals(nyc_predict_clean$white_count, yrep = predictions_negbin,
prob_outer = 0.95) +
ggplot2::scale_x_continuous(
labels = nyc_predict_clean$nta_id,
breaks = 1:nrow(nyc_predict_clean)) +
xaxis_text(angle = 90, hjust = 1) +
theme_linedraw()+
theme(panel.grid.major = element_line("transparent"),
axis.title.y = element_blank(),
axis.text.y = element_blank())+
coord_flip()
Models 2 & 3: Hierarchy by Neighborhood
poisson_hierarchical <- stan_glmer(
white_count ~ total_pop + mean_income + mean_rent +
unemployment_count + sub_count +
perc_covered_by_transit + school_count +
store_count + sub_count + bus_count +
eviction_count + uninsured_count + (1|nta_id),
data = nyc_clean,
family = poisson,
chains = 4, iter = 500*2, seed = 84735, refresh = 0
)
negbin_hierarchical <- stan_glmer(
white_count ~ total_pop + mean_income + mean_rent +
unemployment_count + sub_count +
perc_covered_by_transit + school_count +
store_count + sub_count + bus_count +
eviction_count + uninsured_count + (1|nta_id),
data = nyc_clean,
family = neg_binomial_2,
chains = 4, iter = 500*2, seed = 84735, refresh = 0
)check_1 <- pp_check(poisson_hierarchical) +
xlab("White Resident Count") +
labs(title = "Poisson")+
xlim(0,100000)+
theme(plot.title = element_text(face="bold", size=25, hjust=.5))
check_2 <- pp_check(negbin_hierarchical) +
xlab("White Resident Count") +
xlim(0,100000)+
labs(title = "Negative Binomial")+
theme(plot.title = element_text(face="bold", size=25, hjust=.5))
ggpubr::ggarrange(check_1, check_2, ncol=2, common.legend=TRUE, legend = "right")
library(kableExtra)
tidy(poisson_hierarchical, effects = "fixed", conf.int = TRUE, conf.level = 0.95) %>%
mutate(estimate= ifelse(term == "(Intercept)", exp(estimate), (exp(estimate)-1)*100),
conf.low= ifelse(term == "(Intercept)", exp(conf.low), (exp(conf.low)-1)*100),
conf.high = ifelse(term == "(Intercept)", exp(conf.high), (exp(conf.high)-1)*100))%>%
filter(conf.low > 0 & conf.high > 0 | conf.low < 0 & conf.high < 0) %>%
kable(align = "c", caption = "Hierarchicahl Poisson - Model Summary") %>%
kable_styling()| term | estimate | std.error | conf.low | conf.high |
|---|---|---|---|---|
| (Intercept) | 1717.1091298 | 0.3536747 | 885.2237713 | 3675.8332898 |
| total_pop | 0.0067707 | 0.0000086 | 0.0050549 | 0.0085170 |
| mean_income | 0.0010414 | 0.0000054 | 0.0000112 | 0.0021181 |
| eviction_count | -0.3087356 | 0.0005409 | -0.4098675 | -0.1987937 |
| uninsured_count | -0.0162629 | 0.0000601 | -0.0274034 | -0.0045796 |
tidy(negbin_hierarchical, effects = "fixed", conf.int = TRUE, conf.level = 0.95)%>%
mutate(estimate= ifelse(term == "(Intercept)", exp(estimate), (exp(estimate)-1)*100),
conf.low= ifelse(term == "(Intercept)", exp(conf.low), (exp(conf.low)-1)*100),
conf.high = ifelse(term == "(Intercept)", exp(conf.high), (exp(conf.high)-1)*100))%>%
filter(conf.low > 0 & conf.high > 0 | conf.low < 0 & conf.high < 0) %>%
kable(align = "c", caption = "Hierarchichal Negative Binomial - Model Summary") %>%
kable_styling()| term | estimate | std.error | conf.low | conf.high |
|---|---|---|---|---|
| (Intercept) | 2862.8732672 | 0.3439035 | 1459.0548198 | 5555.1716654 |
| total_pop | 0.0060187 | 0.0000086 | 0.0044313 | 0.0077559 |
| mean_income | 0.0010984 | 0.0000048 | 0.0001373 | 0.0021208 |
| eviction_count | -0.2514231 | 0.0004662 | -0.3436878 | -0.1586808 |
| uninsured_count | -0.0177119 | 0.0000511 | -0.0279320 | -0.0078445 |
nyc_predict_clean <- nyc_clean %>%
na.omit()
set.seed(84735)
predictions_poisson <- posterior_predict(
poisson_hierarchical, newdata = nyc_predict_clean)
predictions_negbin <- posterior_predict(
negbin_hierarchical, newdata = nyc_predict_clean)
library(tidybayes)
library(bayesplot)
ppc_intervals(nyc_predict_clean$white_count, yrep = predictions_poisson,
prob_outer = 0.95) +
ggplot2::scale_x_continuous(
labels = nyc_predict_clean$nta_id,
breaks = 1:nrow(nyc_predict_clean)) +
xaxis_text(angle = 90, hjust = 1) +
theme_linedraw()+
theme(panel.grid.major = element_line("transparent"),
axis.title.y = element_blank(),
axis.text.y = element_blank())+
coord_flip() 
ppc_intervals(nyc_predict_clean$white_count, yrep = predictions_negbin,
prob_outer = 0.95) +
ggplot2::scale_x_continuous(
labels = nyc_predict_clean$nta_id,
breaks = 1:nrow(nyc_predict_clean)) +
xaxis_text(angle = 90, hjust = 1) +
theme_linedraw()+
theme(panel.grid.major = element_line("transparent"),
axis.title.y = element_blank(),
axis.text.y = element_blank())+
coord_flip()